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        "author": "Dawid Laszuk",
        "author_email": "dawid@lasz.uk",
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        "classifiers": [
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            "Topic :: Scientific/Engineering",
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            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "[![codecov](https://codecov.io/gh/laszukdawid/PyEMD/branch/master/graph/badge.svg)](https://codecov.io/gh/laszukdawid/PyEMD)\n[![BuildStatus](https://travis-ci.org/laszukdawid/PyEMD.png?branch=master)](https://travis-ci.org/laszukdawid/PyEMD)\n[![DocStatus](https://readthedocs.org/projects/pyemd/badge/?version=latest)](https://pyemd.readthedocs.io/)\n[![Codacy](https://api.codacy.com/project/badge/Grade/5385d5ddc8e84908bd4e38f325443a21)](https://www.codacy.com/app/laszukdawid/PyEMD?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/PyEMD&utm_campaign=badger)\n\n# PyEMD\n\n## Links\n\n-   HTML documentation: <https://pyemd.readthedocs.org>\n-   Issue tracker: <https://github.com/laszukdawid/pyemd/issues>\n-   Source code repository: <https://github.com/laszukdawid/pyemd>\n\n## Introduction\n\nThis is yet another Python implementation of Empirical Mode\nDecomposition (EMD). The package contains many EMD variations and\nintends to deliver more in time.\n\n### EMD variations:\n* Ensemble EMD (EEMD),\n* \"Complete Ensemble EMD\" (CEEMDAN)\n* different settings and configurations of vanilla EMD.\n* Image decomposition (EMD2D & BEMD) (experimental)\n\n*PyEMD* allows to use different splines for envelopes, stopping criteria\nand extrema interpolation.\n\n### Available splines:\n* Natural cubic [default]\n* Pointwise cubic\n* Akima\n* Linear\n\n### Available stopping criteria:\n* Cauchy convergence [default]\n* Fixed number of iterations\n* Number of consecutive proto-imfs\n\n### Extrema detection:\n* Discrete extrema [default]\n* Parabolic interpolation\n\n## Installation\n\n### Recommended\n\nSimply download this directory either directly from GitHub, or using\ncommand line:\n\n> \\$ git clone <https://github.com/laszukdawid/PyEMD>\n\nThen go into the downloaded project and run from command line:\n\n> \\$ python setup.py install\n\n### PyPi\n\nPackaged obtained from PyPi is/will be slightly behind this project, so\nsome features might not be the same. However, it seems to be the\neasiest/nicest way of installing any Python packages, so why not this\none?\n\n> \\$ pip install EMD-signal\n\n## Example\n\nMore detailed examples are included in the\n[documentation](https://pyemd.readthedocs.io/en/latest/examples.html) or\nin the\n[PyEMD/examples](https://github.com/laszukdawid/PyEMD/tree/master/example).\n\n### EMD\n\nIn most cases default settings are enough. Simply import `EMD` and pass\nyour signal to instance or to `emd()` method.\n\n```python\nfrom PyEMD import EMD\nimport numpy as np\n\ns = np.random.random(100)\nemd = EMD()\nIMFs = emd(s)\n```\n\nThe Figure below was produced with input:\n$S(t) = cos(22 \\pi t^2) + 6t^2$\n\n![simpleExample](https://github.com/laszukdawid/PyEMD/raw/master/example/simple_example.png?raw=true)\n\n### EEMD\n\nSimplest case of using Ensemble EMD (EEMD) is by importing `EEMD` and\npassing your signal to the instance or `eemd()` method.\n\n```python\nfrom PyEMD import EEMD\nimport numpy as np\n\ns = np.random.random(100)\neemd = EEMD()\neIMFs = eemd(s)\n```\n\n### CEEMDAN\n\nAs with previous methods, there is also simple way to use `CEEMDAN`.\n\n```python\nfrom PyEMD import CEEMDAN\nimport numpy as np\n\ns = np.random.random(100)\nceemdan = CEEMDAN()\ncIMFs = ceemdan(s)\n```\n\n### Visualisation\n\nThe package contain a simple visualisation helper that can help, e.g., with time series and instantaneous frequencies.\n\n```python\nimport numpy as np\nfrom PyEMD import EMD, Visualisation\n\nt = np.arange(0, 3, 0.01)\nS = np.sin(13*t + 0.2*t**1.4) - np.cos(3*t)\n\n# Extract imfs and residue\n# In case of EMD\nemd = EMD()\nemd.emd(S)\nimfs, res = emd.get_imfs_and_residue()\n\n# In general:\n#components = EEMD()(S)\n#imfs, res = components[:-1], components[-1]\n\nvis = Visualisation()\nvis.plot_imfs(imfs=imfs, residue=res, t=t, include_residue=True)\nvis.plot_instant_freq(t, imfs=imfs)\nvis.show()\n```\n\n### EMD2D/BEMD\n\n*Unfortunately, this is Experimental and we can't guarantee that the output is meaningful.*\nThe simplest use is to pass image as monochromatic numpy 2D array. Sample as\nwith the other modules one can use the default setting of an instance or, more explicitly,\nuse the `emd2d()` method.\n\n```python\nfrom PyEMD import EMD2D  #, BEMD\nimport numpy as np\n\nx, y = np.arange(128), np.arange(128).reshape((-1,1))\nimg = np.sin(0.1*x)*np.cos(0.2*y)\nemd2d = EMD2D()  # BEMD() also works\nIMFs_2D = emd2d(img)\n```\n\n## Contact\n\nFeel free to contact me with any questions, requests or simply to say\n*hi*. It's always nice to know that I one's work have eased others and saved\nsomeone's time. Contributing to the project is also acceptable.\n\nContact me either through gmail (laszukdawid @ gmail) or search me through your\nfavourite web search.\n\n### Citation\n\nIf you found this package useful and would like to cite it in your work\nplease use following structure:\n\nDawid Laszuk (2017-), **Python implementation of Empirical Mode\nDecomposition algorithm**. <http://www.laszukdawid.com/codes>.\n\n",
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